Question Answering and Semantic Understanding aims to develop systems that can comprehend the meaning of natural language questions and provide accurate and relevant answers.

Question Answering systems often employ a combination of keyword matching, machine learning, and rule-based reasoning to understand and answer questions.

Semantic understanding plays a crucial role in Question Answering by helping systems understand the meaning of questions, identify relevant information, and generate accurate answers.

SQuAD, MS MARCO, and TriviaQA are popular datasets used to evaluate the performance of Question Answering systems.

Open-domain Question Answering systems face challenges due to the vastness of knowledge required, the ambiguity of natural language questions, and the need for real-time response generation.

Accuracy, F1 score, and Mean Reciprocal Rank (MRR) are commonly used metrics for evaluating the performance of Question Answering systems.

Context plays a crucial role in Question Answering by providing additional information, disambiguating meaning, and helping identify relevant sources for answering questions.

Closed-domain Question Answering systems are designed to answer questions within a specific domain or context, using a limited set of predefined facts.

Closed-domain Question Answering systems often face the challenge of limited training data, which can hinder their ability to generalize to new questions.

Closed-domain Question Answering systems often employ a combination of rule-based reasoning, machine learning, and information retrieval techniques to answer questions.

Open-domain Question Answering systems face challenges due to the wide variety of questions they need to handle, the lack of sufficient training data, and the difficulty in understanding the user's intent.

Open-domain Question Answering systems often employ a combination of machine learning, information retrieval, and knowledge graph construction techniques to answer questions.

Knowledge graphs play a crucial role in Question Answering by providing a structured representation of knowledge, facilitating the linking of related concepts and entities, and enabling reasoning and inference over knowledge.

Knowledge graph construction faces challenges due to the need to integrate knowledge from multiple sources, the difficulty in resolving entity ambiguity, and the lack of sufficient training data.

Semantic understanding in Question Answering aims to extract key concepts and relationships, identify the expected answer type, and retrieve relevant information from a knowledge base.